Show simple item record

dc.contributor.authorKral, Zachary Tyler
dc.contributor.authorHorn, Walter J.
dc.contributor.authorSteck, James E.
dc.date.accessioned2013-09-26T19:57:44Z
dc.date.available2013-09-26T19:57:44Z
dc.date.issued2013
dc.identifier.citationZachary Kral, Walter Horn, and James Steck, “Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems,” The Scientific World Journal, vol. 2013, Article ID 823603, 13 pages, 2013. doi:10.1155/2013/823603en_US
dc.identifier.issn1537-744X
dc.identifier.otherWOS:000323958800001
dc.identifier.urihttp://dx.doi.org/10.1155/2013/823603
dc.identifier.urihttp://hdl.handle.net/10057/6512
dc.descriptionCopyright © 2013 Zachary Kral et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.description.abstractAerospace systems are expected to remain in service well beyond their designed life. Consequently, maintenance is an important issue. A novel method of implementing artificial neural networks and acoustic emission sensors to form a structural health monitoring (SHM) system for aerospace inspection routines was the focus of this research. Simple structural elements, consisting of flat aluminum plates of AL 2024-T3, were subjected to increasing static tensile loading. As the loading increased, designed cracks extended in length, releasing strain waves in the process. Strain wave signals, measured by acoustic emission sensors, were further analyzed in post-processing by artificial neural networks (ANN). Several experiments were performed to determine the severity and location of the crack extensions in the structure. ANNs were trained on a portion of the data acquired by the sensors and the ANNs were then validated with the remaining data. The combination of a system of acoustic emission sensors, and an ANN could determine crack extension accurately. The difference between predicted and actual crack extensions was determined to be between 0.004 in. and 0.015 in. with 95% confidence. These ANNs, coupled with acoustic emission sensors, showed promise for the creation of an SHM system for aerospace systems.en_US
dc.description.sponsorshipDepartment of Energy for its support (DOE DE-FG36-08GO88149).en_US
dc.language.isoen_USen_US
dc.publisherHindawi Publishing Corporationen_US
dc.relation.ispartofseriesScientific World Journal;v.2013:article ID 823603
dc.titleCrack propagation analysis using acoustic emission sensors for structural health monitoring systemsen_US
dc.typeArticleen_US
dc.rights.holderCopyright © 2013 Zachary Kral et al.


Files in this item

Thumbnail

This item appears in the following Collection(s)

  • AE Research Publications
    Research publications authored by the Department of Aerospace Engineering faculty and graduate students.

Show simple item record